Real-time mobile gait analysis device using sock with attached gyro sensor and method thereof

Abstract

The present invention relates to a real-time mobile gait analysis device using a sock with an attached gyro sensor, and a method thereof. The real-time mobile gait analysis device may comprise: a plurality of gyro sensors configured to measure angular velocity; a sock that can be worn on a foot by a patient and includes a fixing unit which can fix the plurality of gyro sensors; a communication module for receiving data measured by the plurality of gyro sensors; and a processor for receiving pre-operative movement data of the patient measured through the plurality of gyro sensors, receiving post-operative movement data of the patient measured through the plurality of gyro sensors, and generating evaluation data for evaluating the result of an operation on the basis of the pre-operative and post-operative movement data of the patient.

Description

Real-time mobile gait analysis device using socks with attached gyro sensors and method therefor

[0001] The present disclosure relates to a real-time mobile gait analysis device and method using a sock with a gyro sensor attached thereto.

[0002] Conventional mobile gait analysis devices primarily measure plantar pressure. This involves embedding pressure sensors in shoe insoles to detect changes in pressure distribution across the sole of the foot during walking and quantifying these changes into designated indices. However, this approach, which relies solely on pressure sensors placed within shoe insoles, presents technical limitations in comprehensively analyzing gait indices occurring within the various joints of the ankle and foot.

[0003] Specifically, in order to plan surgical treatment for patients seeking to correct gait abnormalities or to analyze the patient's gait after surgery and understand the changes in the gait, it is difficult to conduct an accurate analysis based only on changes in the pressure distribution on the sole of the foot.

[0004] To accurately analyze a patient's gait after surgery, it is necessary to be able to detect changes in indicators during standing and walking in anatomically important parts of the patient's major joints or small joints below the ankle.

[0005] While existing gait analysis equipment exists, it requires a certain amount of space for 3D imaging, requires complex external attachments, and often incorporates protective equipment. This makes it uncomfortable to wear on a daily basis, limiting accessibility and compliance. Consequently, these devices are often restricted to laboratory or specialized clinics, making it difficult to capture gait pattern data from subjects' natural, everyday environments.

[0006] Accordingly, there is a need for a device and method capable of analyzing the gait of a subject who complains of pain or discomfort in their daily life environment and seeks to resolve it.

[0007] The present disclosure aims to provide a gait analysis device and method capable of analyzing a patient's gait in real time in a daily life environment.

[0008] The problems to be solved by the present disclosure are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

[0009] In order to achieve the above-described technical problem, a real-time mobile gait analysis device utilizing a sock having a gyro sensor attached thereto according to an embodiment of the present disclosure comprises: a plurality of gyro sensors configured to measure angular velocity; a sock that can be worn on a foot by a patient and includes a fixing part capable of fixing the plurality of gyro sensors; a communication module that receives data measured by the plurality of gyro sensors; a memory in which at least one process for gait analysis of the patient is stored; and a processor that performs the gait analysis according to the process, wherein the processor receives movement data of the patient before surgery measured through the plurality of gyro sensors, receives movement data of the patient after surgery measured through the plurality of gyro sensors, and generates evaluation data for evaluating the results of the surgery based on the movement data of the patient before and after the surgery.

[0010] According to an embodiment of the present disclosure, each of the plurality of gyro sensors may be attached to the fixing member so as to be fixed on a specific body part of the patient while the patient is wearing the sock.

[0011] According to an embodiment of the present disclosure, the body part of the patient to which the plurality of gyro sensors are fixed may be at least one of the ankle, below the ankle, instep, heel, and big toe.

[0012] According to an embodiment of the present disclosure, the fixing member may be formed of a magnet having a larger area than the plurality of gyro sensors, and each of the plurality of gyro sensors may be formed to be attachable to the magnet.

[0013] According to an embodiment of the present disclosure, the processor may output either the surgical result being good or the surgical result requiring additional treatment as evaluation data for evaluating the surgical result.

[0014] According to an embodiment of the present disclosure, the processor may determine that the surgical result is good if the amount of change in the movement data corresponding to the ankle and the big toe among the movement data of the patient before and after the surgical operation is greater than a reference value.

[0015] According to an embodiment of the present disclosure, the processor may determine that the surgical result is good when the amount of change in the movement data corresponding to the heel, the instep, and the bottom of the ankle among the movement data of the patient before and after the surgery is less than the reference value.

[0016] In addition, the present disclosure provides a real-time mobile gait analysis method performed by a processor of a device including a plurality of gyro sensors and a sock that can be worn on a patient's foot and that can fix the plurality of gyro sensors, the method including: a step of the processor receiving movement data of the patient before surgery measured through the plurality of gyro sensors; a step of the processor receiving movement data of the patient after surgery measured through the plurality of gyro sensors; and a step of the processor generating evaluation data for evaluating the outcome of the surgery based on the movement data of the patient before and after the surgery.

[0017] In addition, a computer program stored in a computer-readable recording medium for implementing the present disclosure may be further provided.

[0018] In addition, a computer-readable recording medium recording a computer program for implementing the present disclosure may be further provided.

[0019] The gait analysis device and method according to the present disclosure can analyze the gait of a patient by utilizing a gyro sensor attached to a sock that the patient wears on a daily basis, thereby increasing the accessibility and compliance of the examination.

[0020] The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

[0021] Figure 1 is an overall system diagram of the present disclosure.

[0022] Figure 2 is a block diagram of a server included in the gait analysis device of the present disclosure.

[0023] Figure 3 is a block diagram of a terminal included in the gait analysis device of the present disclosure.

[0024] FIG. 4 is an exemplary diagram showing a gyro sensor attached to a sock according to the present disclosure.

[0025] Figure 5 is a drawing illustrating a plurality of fixing parts included in a sock.

[0026] Fig. 6 is a drawing illustrating a gyro sensor attached to the fixed part of Fig. 5.

[0027] Figures 7 and 8 are exemplary drawings showing in detail each part to which multiple gyro sensors are attached in Figures 5 and 6.

[0028] Figure 9 is a flowchart illustrating a gait analysis method according to the present disclosure.

[0029] Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and any content that is common in the technical field to which this disclosure belongs or that overlaps between embodiments is omitted. The terms "part, module, element, block" used in the specification may be implemented in software or hardware, and depending on the embodiment, multiple "parts, modules, elements, blocks" may be implemented as a single component, or a single "part, module, element, block" may include multiple components. Throughout the specification, when a part is said to be "connected" to another part, this includes not only cases where it is directly connected, but also cases where it is indirectly connected, and an indirect connection includes a connection via a wireless communication network.

[0030] Additionally, when a part is said to "include" a component, this does not mean that it excludes other components, but rather that it may include other components, unless otherwise specifically stated.

[0031] Throughout the specification, when we say that an element is "on" another element, this includes not only cases where the element is in contact with the other element, but also cases where another element exists between the two elements.

[0032] The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

[0033] Singular expressions include plural expressions unless the context clearly indicates otherwise.

[0034] The identification codes for each step are used for convenience of explanation and do not describe the order of each step. Each step may be performed in a different order than specified unless the context clearly indicates a specific order.

[0035] The operating principle and embodiments of the present disclosure are described below with reference to the attached drawings.

[0036] As used herein, the term "device according to the present disclosure" encompasses a variety of devices capable of performing computational processing and providing results to a user. For example, the device according to the present disclosure may include a computer, a server device, and a portable terminal, or may be any one of them.

[0037] Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

[0038] The above server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server.

[0039] The above portable terminal may include, for example, a wireless communication device that ensures portability and mobility, and may include all kinds of handheld-based wireless communication devices such as a PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminal, a smart phone, and a wearable device such as a watch, a ring, a bracelet, an anklet, a necklace, glasses, contact lenses, or a head-mounted device (HMD).

[0040] The gait analysis device according to the present disclosure may be implemented by at least one of a server and a terminal. Specifically, the device according to the present disclosure may be implemented by either the server or the terminal, or may be implemented as a system through data transmission and reception between the server and the terminal.

[0041] Below, a gait analysis device according to the present disclosure is described.

[0042] Referring to FIG. 1, a device according to the present disclosure may include a server (10), a terminal (20), and a sensor (30).

[0043] At least one of the terminal (20) and the server (30) and the sensor (30) are connected to a network, and the server (10) or the terminal can receive data required for analysis from the sensor (30) and then generate analysis results. When the server (30) generates analysis results, the server (30) can receive data measured by the sensor (30) from the terminal (20), and then generate analysis results based on the data and transmit the analysis results to the terminal (20).

[0044] Meanwhile, it is obvious to those skilled in the art that the terminal (20) is not limited to the above-described portable terminal, and may include a processor-equipped notebook, desktop, laptop, tablet PC, slate PC, etc.

[0045] As described above, the gait analysis device according to the present disclosure can be implemented through data transmission and reception between a server (10), a terminal (20), and a server (30).

[0046] Below, each of the server (10), terminal (20), and sensor (30) for implementing a gait analysis device according to the present disclosure will be described.

[0047] Figure 2 is a block diagram of a server included in the gait analysis device of the present disclosure.

[0048] A server (100) according to the present disclosure may include at least one of a communication module (110), a memory (120), and a processor (130).

[0049] The communication module (110) can communicate with at least one of a terminal, an external storage (e.g., a database (140)), an external server, and a cloud server.

[0050] Meanwhile, an external server or cloud server may be configured to perform at least a portion of the role of the processor (130). That is, data processing or data operations, etc. may be performed on an external server or cloud server, and the present invention does not impose any particular limitations on this method.

[0051] Meanwhile, the communication module (110) can support various communication methods according to the communication standards of the communicating target (e.g., electronic device, external server, device, etc.).

[0052] For example, the communication module (110) may be configured to communicate with a communication target using at least one of WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Wi-Fi (Wireless Fidelity) Direct, DLNA (Digital Living Network Alliance), WiBro (Wireless Broadband), WiMAX (World Interoperability for Microwave Access), HSDPA (High Speed ​​Downlink Packet Access), HSUPA (High Speed ​​Uplink Packet Access), LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced), 5G (5th Generation Mobile Telecommunication), Bluetooth™, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), UWB (Ultra-Wideband), ZigBee, NFC (Near Field Communication), Wi-Fi Direct, and Wireless USB (Wireless Universal Serial Bus) technologies.

[0053] Next, the memory (120) may be configured to store various information related to the present invention. In the present invention, the memory (120) may be provided in the device itself according to the present invention. Alternatively, at least a portion of the memory (120) may refer to at least one of a database (DB, 140) and a cloud storage (or cloud server). That is, the memory (120) may be sufficient as long as it stores information necessary for the device and method according to the present invention, and it can be understood that there are no restrictions on the physical space. Accordingly, in the following, the memory (120), the database (140), the external storage, and the cloud storage (or cloud server) will not be separately distinguished, and will all be referred to as the memory (120).

[0054] Next, the processor (130) may be configured to control the overall operation of the device related to the present invention. The processor (130) may process signals, data, information, etc. input or output through the components discussed above, or provide or process appropriate information or functions to the user.

[0055] The processor (130) includes at least one CPU (Central Processing Unit) and can perform functions according to the present invention.

[0056] At least one component may be added or deleted to correspond to the performance of the components illustrated in FIG. 2. Furthermore, it will be readily apparent to those skilled in the art that the relative positions of the components may be altered to correspond to the performance or structure of the device.

[0057] Hereinafter, the terminal included in the device of the present disclosure will be described in detail.

[0058] Figure 3 is a block diagram of a terminal included in the device of the present disclosure.

[0059] Referring to FIG. 3, a terminal (200) according to the present disclosure may include a communication module (210), an input unit (220), a display (230), a processor (240), etc. The components illustrated in FIG. 3 are not essential for implementing a device according to the present disclosure, and thus, the terminal described in this specification may have more or fewer components than the components listed above.

[0060] Among the above components, the communication module (210) may include one or more components that enable communication with an external device, and may include, for example, at least one of a broadcast reception module, a wired communication module, a wireless communication module, a short-range communication module, and a location information module.

[0061] The wired communication module may include various wired communication modules such as a Local Area Network (LAN) module, a Wide Area Network (WAN) module, or a Value Added Network (VAN) module, as well as various cable communication modules such as a Universal Serial Bus (USB), a High Definition Multimedia Interface (HDMI), a Digital Visual Interface (DVI), RS-1302 (recommended standard 1302), power line communication, or plain old telephone service (POTS).

[0062] The wireless communication module may include a wireless communication module that supports various wireless communication methods such as GSM (global System for Mobile Communication), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), UMTS (universal mobile telecommunications system), TDMA (Time Division Multiple Access), LTE (Long Term Evolution), 4G, 5G, and 6G, in addition to a WiFi module and a Wireless Broadband module.

[0063] The input module (220) is for inputting image information (or signal), audio information (or signal), data, or information input from a user, and may include at least one camera, at least one microphone, and at least one user input unit. Voice data or image data collected from the input unit may be analyzed and processed into a user control command.

[0064] The display (230) can be formed as a touch screen by forming a mutual layer structure with the touch sensor or by forming an integral structure. Such a touch screen can function as a user input unit that provides an input interface between the device and the user, and at the same time, can provide an output interface between the device and the user.

[0065] The display (230) displays (outputs) information processed in the terminal (200). For example, the display unit may display execution screen information of an application program (e.g., an application) running on the device, or UI (User Interface) or GUI (Graphical User Interface) information according to such execution screen information.

[0066] In addition to the above-described components, the above-described terminal (200) may further include an interface unit and a memory.

[0067] The above interface unit serves as a passage for various types of external devices connected to the terminal (200). This interface unit may include at least one of a wired / wireless headset port, an external charger port, a wired / wireless data port, a memory card port, a port for connecting a device equipped with an identification module (SIM), an audio I / O (Input / Output) port, a video I / O (Input / Output) port, and an earphone port. The present device may perform appropriate control related to an external device connected to the interface unit.

[0068] The above memory can store data supporting various functions of the terminal (200), programs for the operation of the processor, can store input / output data (e.g., music files, still images, videos, etc.), and can store a plurality of application programs (or applications) running on the terminal (200), data for the operation of the terminal (200), and commands. At least some of these application programs can be downloaded from an external server via wireless communication.

[0069] The memory may include at least one type of storage medium among a flash memory type, a hard disk type, an SSD (Solid State Disk type), an SDD (Silicon Disk Drive type), a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, and an optical disk. In addition, the memory may be a database that is separate from the terminal (200) but is connected by wire or wirelessly.

[0070] Meanwhile, the terminal (200) described above includes a processor (240).

[0071] Meanwhile, the processor (130, 240) included in at least one of the server (100) and the terminal (200) may include an artificial intelligence module for implementing a gait analysis device to be described later. The artificial intelligence model learning method described later is described as being implemented by the operation of the module, but the performance of each step described later need not necessarily be performed by the module.

[0072] Additionally, the processor may control any one or a combination of the components described above to implement various embodiments of the present disclosure described in the drawings below on the device.

[0073] Hereinafter, the terminal included in the device of the present disclosure will be described in detail.

[0074] FIG. 4 is an exemplary diagram showing a gyro sensor attached to a sock according to the present disclosure.

[0075] The device of the present disclosure includes a plurality of gyro sensors (30) configured to measure angular velocity.

[0076] The angular velocity measured by the gyro sensor can define the movement of the object to which the gyro sensor is attached.

[0077] For example, when a gyro sensor is attached to a patient's ankle, data measured by the gyro sensor can generate movement data corresponding to the patient's ankle.

[0078] In this specification, data generated by a gyro sensor is referred to as motion data. Motion data may be data defining the angular velocity or movement of an object to which a gyro sensor is attached.

[0079] A gyro sensor can be attached to a specific body part of a patient and generate movement data corresponding to that specific body part.

[0080] A plurality of gyro sensors (30) are fixed to different body parts of the patient, respectively, to generate movement data for each of the plurality of body parts.

[0081] For example, referring to FIG. 4, the plurality of gyro sensors (30) may include a sensor (30a) fixed to the heel of the patient, a sensor (30b) fixed to the lower ankle, a sensor (30c) fixed to the ankle, a sensor (30d) fixed to the instep, and a sensor (30e) fixed to the big toe. In this case, each of the plurality of gyro sensors (30a, 30b, 30c, 30d, 30e) may generate movement data for the heel, lower ankle, ankle, instep, and big toe of the patient, respectively.

[0082] Meanwhile, the gait analysis device according to the present disclosure is configured to be worn by a patient on the foot and includes a sock including a fixing part capable of fixing the gyro sensor.

[0083] A plurality of gyro sensors (30) may be fixed to the above socks. The plurality of gyro sensors (30) may be fixed to the socks so that the gyro sensors are positioned at specific body parts of the patient when the patient wears the socks.

[0084] For example, referring to FIG. 4, five gyro sensors (30a, 30b, 30c, 30d, 30e) can be fixed to the sock, and when the patient wears the sock, the fixing positions of the gyro sensors (30a, 30b, 30c, 30d, 30e) can be set so that the gyro sensors (30a, 30b, 30c, 30d, 30e) are located on the patient's heel, under the ankle, ankle, instep, and big toe, respectively.

[0085] The sock may include a fixing member for fixing the gyro sensor. The fixing member allows the gyro sensor to be fixed to a specific body part of the patient while the patient is wearing the sock.

[0086] In one embodiment, the fixing member may be formed of a magnet and may be formed with a larger area than the gyro sensor. Meanwhile, each of the plurality of gyro sensors (30a, 30b, 30c, 30d, 30e) may be configured to be attachable to the magnet. In this case, the gyro sensor may not only be fixed to a specific fixed location, but may also vary in location within the area range of the fixing member. This allows the gyro sensor to be fixed to the correct measurement target area even when patients with different body sizes wear the socks according to the present disclosure.

[0087] Figure 5 is a drawing illustrating a plurality of fixing parts included in a sock.

[0088] Fig. 6 is a drawing illustrating a gyro sensor attached to the fixed part of Fig. 5.

[0089] Figures 7 and 8 are exemplary drawings showing in detail each part to which multiple gyro sensors are attached in Figures 5 and 6.

[0090] Each position will be explained with reference to (a) and (b) of Fig. 5.

[0091] The first fixed part (501) is formed to correspond to the LM (lateral malleolus) position, where LM means the lateral malleolus.

[0092] The second fixed part (502) is formed to correspond to the MM (medial malleolus) position, where MM means the medial malleolus.

[0093] The third fixed part (503) is formed to correspond to the TH (talar head) position, where TH refers to the talar head. The talar head is the head of the talus, one of the ankle bones, and is connected to the tibia and fibula to form the ankle joint.

[0094] The fourth fixed part (504) is formed to correspond to the CB (cuboid) position, where CB refers to the cuboid bone. The cuboid bone is one of the bones that constitute the outer arch of the foot and connects the calcaneus and the fifth metatarsal bone.

[0095] The fifth fixed part (505) is formed to correspond to the 5MTH (fifth metatarsal head) position, and 5MTH means the fifth metatarsal head.

[0096] The sixth fixed part (506) is formed to correspond to the position of 23T (between the heads of the second and third metatarsals and the hallux), and 23T is between the heads of the second and third metatarsals and the hallux.

[0097] The seventh fixed part (507) is attached at the Hallux (big toe) position.

[0098] The 8th fixed part (508) is formed to correspond to the 1MTH (first metatarsal head) position, and 1MTH means the first metatarsal head.

[0099] The ninth fixed part (509) is formed to correspond to the NV (navicular bone) position, where NV means the navicular bone.

[0100] The 10th fixed part (510) is formed to correspond to the TC (top calcaneus) position, where TC refers to the upper part of the calcaneus, the uppermost part of the heel bone.

[0101] The 11th fixed part (511) is formed to correspond to the BC (bottom calcaneus) position, where BC refers to the lower part of the calcaneus, the lowest part of the heel bone.

[0102] Referring to (a) and (b) of FIG. 6, each gyro sensor (601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611) is exemplified as being attached to each fixed part (501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511).

[0103] The gait analysis device according to the present disclosure collects movement data of each body part of a patient while wearing the above-described socks, and analyzes the patient's gait based on the data.

[0104] Meanwhile, at least one component may be added or deleted in accordance with the performance of the components illustrated in FIGS. 1 through 8. Furthermore, it will be readily apparent to those skilled in the art that the relative positions of the components may be altered in accordance with the performance or structure of the device.

[0105] Below, the artificial intelligence described in the present invention is described in detail.

[0106] The artificial intelligence-related functions according to the present disclosure are operated through the processor and memory installed in the above-described server and terminal. The processor may be composed of one or more processors. In this case, one or more processors may be a general-purpose processor such as a CPU, an AP, a DSP (Digital Signal Processor), a graphics-only processor such as a GPU or a VPU (Vision Processing Unit), or an artificial intelligence-only processor such as an NPU. One or more processors control the processing of input data according to predefined operation rules or artificial intelligence models stored in the memory. Alternatively, if one or more processors are artificial intelligence-only processors, the artificial intelligence-only processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0107] The predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that the basic artificial intelligence model is trained using a learning algorithm using a plurality of learning data, thereby creating a predefined operation rules or artificial intelligence model set to perform a desired characteristic (or purpose). This learning may be performed on the device itself on which the artificial intelligence according to the present disclosure is performed, or may be performed through a separate server and / or system. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0108] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values, and performs neural network operations through operations between the operation results of the previous layer and the multiple weights. The multiple weights of the multiple neural network layers may be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model is reduced or minimized during the learning process. The artificial neural network may include a deep neural network (DNN), and examples thereof include, but are not limited to, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or deep Q-networks.

[0109] According to an exemplary embodiment of the present disclosure, a processor can implement artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network that imitates human neurons (biological neurons) to enable machines to learn. Artificial intelligence methodologies can be categorized into supervised learning, in which input data and output data are provided together as learning data depending on the learning method, so that the solution (output data) to the problem (input data) is determined; unsupervised learning, in which only input data is provided without output data, so that the solution (output data) to the problem (input data) is not determined; and reinforcement learning, in which a reward is provided from an external environment whenever an action is taken in the current state, and learning is performed in a direction that maximizes this reward. In addition, artificial intelligence methodologies can be categorized according to the architecture of the learning model. The architectures of widely used deep learning technologies can be categorized into convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs).

[0110] The present device and system may include an artificial intelligence model. The artificial intelligence model may be a single artificial intelligence model or may be implemented as multiple artificial intelligence models. The artificial intelligence model may be composed of a neural network (or artificial neural network) and may include statistical learning algorithms that mimic biological neurons in machine learning and cognitive science. A neural network may refer to a model in general that has problem-solving capabilities by changing the binding strength of synapses through learning, formed by artificial neurons (nodes) that form a network by combining synapses. The neurons of the neural network may include a combination of weights or biases. The neural network may include one or more layers composed of one or more neurons or nodes. For example, the device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device can infer a desired result (output) from an arbitrary input (input) by changing the weights of the neurons through learning.

[0111] The processor can create a neural network, train a neural network, perform a calculation based on received input data, generate an information signal based on the calculation result, or retrain the neural network. The neural network models may include, but are not limited to, various types of models such as CNN (Convolution Neural Network) such as GoogleNet, AlexNet, VGG Network, R-CNN (Region with Convolution Neural Network), RPN (Region Proposal Network), RNN (Recurrent Neural Network), S-DNN (Stacking-based deep Neural Network), S-SDNN (State-Space Dynamic Neural Network), Deconvolution Network, DBN (Deep Belief Network), RBM (Restrcted Boltzman Machine), Fully Convolutional Network, LSTM (Long Short-Term Memory) Network, Classification Network, etc. The processor may include one or more processors for performing calculations according to the neural network models. For example, a neural network may include a deep neural network.

[0112] Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto) Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Depp Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), Generative Adversarial Network (GAN), Liquid State Machine (LSM), Extreme Learning Machine (ELM), It will be understood by those skilled in the art that any neural network may be included, including but not limited to ESN (Echo State Network), DRN (Deep Residual Network), DNC (Differentiable Neural Computer), NTM (Neural Turning Machine), CN (Capsule Network), KN (Kohonen Network), and AN (Attention Network).

[0113] According to an exemplary embodiment of the present disclosure, the processor may be configured to perform a process for generating a CNN (Convolution Neural Network) such as GoogleNet, AlexNet, VGG Network, Region with Convolution Neural Network (R-CNN), Region Proposal Network (RPN), Recurrent Neural Network (RNN), Stacking-based deep Neural Network (S-DNN), State-Space Dynamic Neural Network (S-SDNN), Deconvolution Network, Deep Belief Network (DBN), Restrcted Boltzman Machine (RBM), Fully Convolutional Network, Long Short-Term Memory (LSTM) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC / QA for natural language processing, Text Analysis, Dialog System, GPT-3, GPT-4, Visual Analytics for vision processing, Visual Understanding, Video Synthesis, ResNet for data intelligence, Anomaly Detection, Prediction, Time-Series Forecasting, Various artificial intelligence structures and algorithms, including optimization, recommendation, and data creation, can be utilized, but are not limited thereto. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0114] Below, a gait analysis method utilizing the aforementioned components is described in detail. The gait analysis method described below is implemented through data transmission and reception between the server, terminal, and sensor described above, and some steps of the method may be performed on at least one of the server, terminal, and sensor. However, this is not limited to this, and it will be apparent to those skilled in the art that the method described below may be performed independently on either the server or terminal.

[0115] Below, a gait analysis method utilizing the components described in Figures 1 to 4 is described.

[0116] Figure 9 is a flowchart illustrating a gait analysis method according to the present disclosure.

[0117] Referring to Fig. 9, a step of sensing the patient's movement through a sensor attached to a patient before surgery or a patient with walking abnormality is performed (S110).

[0118] Although this specification describes an embodiment in which the device according to the present disclosure is worn by a patient prior to surgery, it will be apparent to one of ordinary skill in the art that the device according to the present disclosure may also be applied to patients with walking disorders.

[0119] As described in Fig. 4, after having the patient wear a sock with a gyro sensor fixed thereon before surgery, movement data from daily life can be collected.

[0120] In one embodiment, the motion data collected from the patient prior to surgery may be motion data for at least one of the ankle, the lower ankle, the instep, the heel, and the big toe.

[0121] In one embodiment, movement data collected from a patient prior to surgery may be collected within a predetermined time period from the time of surgery. For example, movement data collected from a patient prior to surgery may be data collected within the past week from the time of surgery, or data collected from daily walking for three days.

[0122] Next, a step is performed to sense the patient's movements through a sensor attached to the patient after surgery (S120).

[0123] After surgery, patients wear socks with gyro sensors attached to them, and movement data from their daily lives can be collected.

[0124] In one embodiment, the motion data collected from the patient after surgery may be motion data for at least one of the ankle, the lower ankle, the instep, the heel, and the big toe.

[0125] In one embodiment, movement data collected from a patient after surgery may be collected within a predetermined time period from the time of surgery. For example, movement data collected from a patient after surgery may be collected within a week of the patient becoming able to walk independently after surgery, or may be collected during three days of daily walking.

[0126] Finally, a step is performed to determine the surgical results based on the patient's movements sensed by the sensors before and after the surgery (S130).

[0127] The processor can generate evaluation data to evaluate the surgical outcome based on the patient's movement data.

[0128] The processor can output either the surgical result being good or the surgical result needing additional treatment as evaluation data for evaluating the surgical result.

[0129] In one embodiment, the processor may generate evaluation data by comprehensively considering movement data for at least one of the ankle, the lower ankle, the instep, the heel, and the big toe.

[0130] In one embodiment, the processor may generate evaluation data solely from movement data of body parts (such as the ankle and big toe) for which increased post-operative movement is indicative of a favorable surgical outcome. Specifically, the processor may determine a favorable surgical outcome if the amount of change in movement data corresponding to the ankle and big toe among the patient's pre- and post-operative movement data is greater than a preset reference value.

[0131] In one embodiment, the processor may generate evaluation data based solely on movement data from body parts (heel, instep, and below the ankle) where a small change in movement before and after surgery is indicative of a good surgical outcome. Specifically, the processor may determine a good surgical outcome if the change in movement data corresponding to the heel, instep, and below the ankle among the patient's movement data before and after surgery is less than the reference value.

[0132] In one embodiment, the processor can assign weights to each body part of the patient and generate evaluation data based on the assigned weights and movement data for each body part.

[0133] For example, body parts (ankle and big toe) that can be judged to have a good surgical outcome if their movement increases after surgery can be given a + weight, and body parts (heel, instep, and below the ankle) that can be judged to have a good surgical outcome if their movement change before and after surgery is small can be given a - weight. The processor can apply the above weights to the movement data of each body part, and if the result value is higher than a predetermined value, it can judge the surgical outcome to be good, and if the result value is lower than the predetermined value, it can generate evaluation data that guides the need for additional treatment.

[0134] Meanwhile, the processor may include an artificial intelligence model trained to receive pre- and post-operative patient movement data and generate evaluation data to evaluate the surgical outcome. The artificial intelligence model may be trained based on training data labeled with correct data for the patient's pre- and post-operative movement data.

[0135] Here, the correct answer data can be the patient's medical data after a certain period of time from the time of surgery. For example, if the patient's prognosis is favorable three months after surgery, the correct answer data can define a favorable surgical outcome. If the patient is not fully cured three months later, the correct answer data can define the need for additional treatment.

[0136] As described above, the gait analysis device and method according to the present disclosure can increase the accessibility and compliance of examination by analyzing gait using a gyro sensor attached to a sock that a patient wears on a daily basis.

[0137] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium storing computer-executable instructions. The instructions may be stored in the form of program code, and when executed by a processor, may generate program modules to perform the operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0138] Computer-readable storage media include all types of storage media that store instructions that can be deciphered by a computer. Examples include read-only memory (ROM), random access memory (RAM), magnetic tape, magnetic disks, flash memory, and optical data storage devices.

[0139] The disclosed embodiments have been described with reference to the attached drawings as described above. Those skilled in the art will understand that the present disclosure can be implemented in forms other than the disclosed embodiments without altering the technical spirit or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be construed as limiting.

Claims

1. A plurality of gyro sensors configured to measure angular velocity; A sock that can be worn by a patient on the foot and includes a fixing member capable of fixing the plurality of gyro sensors; A communication module that receives data measured from the plurality of gyro sensors; A memory storing at least one process for gait analysis of the patient; and A processor for performing the gait analysis according to the above process is included, The above processor, Receiving the patient's movement data before surgery measured through the above plurality of gyro sensors, Receiving the patient's movement data after surgery measured through the above plurality of gyro sensors, A real-time mobile gait analysis device characterized in that it generates evaluation data for evaluating the surgical results based on the movement data of the patient before and after the surgical operation.

2. In paragraph 1, Each of the above plurality of gyro sensors, A real-time mobile gait analysis device characterized in that the device is attached to the fixing member so as to be fixed on a specific body part of the patient while the patient is wearing the socks.

3. In paragraph 2, The body part of the patient to which the above plurality of gyro sensors are fixed is, A real-time mobile gait analysis device characterized by at least one of the ankle, the lower ankle, the instep, the heel, and the big toe.

4. In paragraph 3, The above fixed part is made of a magnet with a larger area than the plurality of gyro sensors, A real-time mobile gait analysis device, characterized in that each of the plurality of gyro sensors is attachable to the magnet.

5. In paragraph 4, The above processor, A real-time mobile gait analysis device characterized in that it outputs either the surgical result being good or the need for additional treatment as evaluation data for evaluating the surgical result.

6. In paragraph 5, The above processor, A real-time mobile gait analysis device characterized in that the result of the surgery is judged to be good when the amount of change in the movement data corresponding to the ankle and the big toe among the movement data of the patient before and after the surgery is greater than a reference value.

7. In paragraph 6, The above processor, A real-time mobile gait analysis device characterized in that the surgical result is judged to be good when the amount of change in the movement data corresponding to the heel, the instep, and the lower part of the ankle among the movement data of the patient before and after the surgery is less than the reference value.

8. A real-time mobile gait analysis method performed by a processor of a device including a plurality of gyro sensors and a sock that can be worn on the foot of a patient and capable of fixing the plurality of gyro sensors, A step in which the processor receives movement data of the patient before surgery measured through the plurality of gyro sensors; The step of the processor receiving the movement data of the patient after surgery measured through the plurality of gyro sensors; and A real-time mobile gait analysis device method characterized in that the processor comprises a step of generating evaluation data for evaluating the surgical outcome based on the movement data of the patient before and after the surgical operation.

9. In paragraph 8, Each of the above plurality of gyro sensors, A real-time mobile gait analysis method characterized in that the patient is attached to the fixing member so as to be fixed on a specific body part of the patient while the patient is wearing the socks.

10. In paragraph 9, The body part of the patient to which the above plurality of gyro sensors are fixed is, A real-time mobile gait analysis method characterized by at least one of the ankle, the lower ankle, the instep, the heel, and the big toe.

11. In paragraph 10, The above fixed part is made of a magnet with a larger area than the plurality of gyro sensors, A real-time mobile gait analysis method, characterized in that each of the plurality of gyro sensors is configured to be attachable to the magnet.

12. In paragraph 11, The above processor, A real-time mobile gait analysis method characterized in that it outputs either the surgical result being good or the need for additional treatment as evaluation data for evaluating the surgical result.

13. In paragraph 12, The above processor, A real-time mobile gait analysis method characterized in that the result of the surgery is judged to be good when the amount of change in the movement data corresponding to the ankle and the big toe among the movement data of the patient before and after the surgery is greater than a reference value.

14. In paragraph 13, The above processor, A real-time mobile gait analysis method characterized in that the surgical result is judged to be good when the amount of change in the movement data corresponding to the heel, the instep, and the lower part of the ankle among the movement data of the patient before and after the surgery is less than the reference value.

Images